CN115879824A - Method, device, equipment and medium for assisting expert decision based on ensemble learning - Google Patents
Method, device, equipment and medium for assisting expert decision based on ensemble learning Download PDFInfo
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Abstract
The application provides an expert-assisted decision method, device, equipment and medium based on ensemble learning, wherein an initial decision result is formed by preprocessing an input data index; training by utilizing three integrated learning models through input dimensional features and initial decision results to form model decision results; determining a rule of data scaling through statistical analysis of data indexes to finely adjust an initial decision result and form a result candidate set with a model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data. The method can enable the result distribution to be more in line with the actual situation, improves the situation that the result distribution is excessively concentrated, and reliably assists the expert in making a decision.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to an expert assistant decision-making method, device, equipment and medium based on ensemble learning.
Background
Aiming at scientific research performance, more indexes are constructed and management functions are realized, the functions are more primary, the function of assisting the decision of experts cannot be realized, along with the wide application of machine learning, the experience of the experts in the aspect of scientific research performance evaluation is quantized by utilizing the algorithm of the machine learning, the evaluation result of the scientific research performance can be quickly produced through a model, the experts are assisted in judging the hard indexes, and reference is provided for the future talent introduction and talent selection decisions, so that the whole decision time and cost are saved, and the decision efficiency is improved.
Disclosure of Invention
In view of the above-mentioned shortcomings in the prior art, it is an object of the present application to provide a method, apparatus, device and medium for assisting expert decision based on ensemble learning to solve at least one problem existing in the prior art.
To achieve the above and other related objects, the present application provides an assistant expert decision method based on ensemble learning, the method comprising: preprocessing an input data index to form an initial decision result; training by utilizing three integrated learning models through input dimensional features and initial decision results to form model decision results; determining a rule of data scaling through statistical analysis of data indexes so as to finely adjust the initial decision result and form a result candidate set with the model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
In an embodiment of the present application, the method for preprocessing the input data index includes any one or more of the following: accumulating the data indexes of multiple dimensions to obtain an initial traditional scientific research performance; converting the age into an age score according to an age conversion formula; and directly giving a final decision result to the talents meeting the preset conditions.
In an embodiment of the present application, the age transformation formula is:
wherein age is age.
In an embodiment of the present application, the data index includes: any one or more of h index score, 1% high cited article score, patent score and total domestic item sum score.
In an embodiment of the application, the training by using three types of ensemble learning models through the input dimensional features and the initial decision result to form a model decision result includes: constructing three ensemble learning models based on random forest of bagging thought, XGBoost of boosting thought and GBDT; by utilizing a grid searching mode, taking the mean square error as an evaluation index of the model, carrying out parameter optimization, and preferably selecting the fitting effect of three ensemble learning models; and storing the three integrated learning models after parameter adjustment, inputting the dimensional characteristics needing to predict the scientific research performance, and simultaneously keeping the model decision results of the three integrated learning models.
In an embodiment of the present application, the rule for determining data scaling through statistical analysis of past data indicators includes:
wherein N is an initial decision result; x is all initial decision results; a is the difference value of the upper limit and the lower limit of the initial decision result interval; and B is the lower limit of the interval of the initial decision result.
In an embodiment of the present application, the method for determining the result of the final expert assistant decision according to the average level of the input global sample data includes any one or more of the following combinations: 1) When the features of one expert with the proportion not less than the first preset proportion are all higher than the average level in the group, judging that the expert is ranked before the group, and taking the optimal value in the candidate set as a final result; 2) When the features of one expert with the proportion not less than the second preset proportion are all lower than the average level in the group, judging that the expert ranks in the group, and taking the worst value in the candidate set as a final result; 3) And when the characteristic condition of an expert does not belong to the above condition, judging that the expert is ranked in the middle in the group, and taking the average level of the candidate set as a final result.
To achieve the above and other related objects, the present application provides an assistant expert decision device based on ensemble learning, the device comprising: the preprocessing module is used for preprocessing the input data indexes to form an initial decision result; the processing module is used for training through input dimensional characteristics and initial decision results by utilizing three integrated learning models to form model decision results; determining a rule of data scaling through statistical analysis of data indexes so as to finely adjust the initial decision result and form a result candidate set with the model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
To achieve the above and other related objects, the present application provides a computer apparatus, comprising: a memory, and a processor; the memory is to store computer instructions; the processor executes computer instructions to implement the method as described above.
To achieve the above and other related objects, the present application provides a computer readable storage medium storing computer instructions which, when executed, perform the method as described above.
In summary, the expert-aided decision making method, device, equipment and medium based on ensemble learning provided by the present application preprocesses the input data indexes to form an initial decision making result; training by utilizing three integrated learning models through input dimensional features and initial decision results to form model decision results; determining a rule of data scaling through statistical analysis of data indexes to finely adjust the initial decision result and form a result candidate set with the model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
The method has the following beneficial effects:
according to the method, on the basis of training by using the integrated learning model, the result distribution is more in line with the actual situation by using a data scaling method, and the situation that the distribution of the output result of the model is excessively concentrated is improved, so that the expert decision is more reliably assisted. The method is easy to implement, is low in cost, avoids the condition that the experts contradict before and after decision making on one hand, and can effectively improve the decision making efficiency of the experts by means of integrated learning on the other hand.
Drawings
FIG. 1 is a flowchart illustrating an expert assistant decision method based on ensemble learning according to an embodiment of the present invention.
FIG. 2 is a block diagram of an expert assistant decision system based on ensemble learning according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following embodiments of the present application are described by specific examples, and other advantages and effects of the present application will be readily apparent to those skilled in the art from the disclosure of the present application. The application is capable of other and different embodiments and its several details are capable of modifications and various changes in detail without departing from the spirit of the application. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only schematic and illustrate the basic idea of the present application, and although the drawings only show the components related to the present application and are not drawn according to the number, shape and size of the components in actual implementation, the type, quantity and proportion of the components in actual implementation may be changed at will, and the layout of the components may be more complex.
As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, components, items, species, and/or groups, but do not preclude the presence, or addition of one or more other features, steps, operations, elements, components, species, and/or groups thereof. The terms "or" and/or "as used herein are to be construed as inclusive or meaning any one or any combination. Thus, "a, B or C" or "a, B and/or C" means "any of the following: a; b; c; a and B; a and C; b and C; A. b and C ". An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some way.
Fig. 1 is a flow chart illustrating an assistant expert decision method based on ensemble learning according to an embodiment of the present application. As shown, the method comprises:
step S101: the input data indicators are preprocessed, including conversion calculations of age, to form an initial decision result.
In one or more embodiments of the present application, the method for preprocessing the input data index includes any one or more of the following:
A. and accumulating the data indexes of multiple dimensions to obtain an initial decision result.
Wherein the data metrics include, but are not limited to: any one or more of h index score, 1% high cited article score, patent score and total domestic item sum score. For example, the initial traditional scientific research performance is obtained by accumulating 12 dimensional indexes such as h index score, 1% higher cited article score, patent score, total domestic item sum score and the like.
The h-index can be used to assess the number of academic yields and the academic yield level of researchers. Briefly, if a h score of 5 indicates that the author has at least 5 papers, and each paper is cited by five citations at least; if the h-score is 10, it means that he has 10 articles, and each article is cited at least 10 times.
B. And converting the age into an age score according to an age conversion formula.
The age conversion formula is:
wherein age is age.
For example, the age data is converted as in the above formula, and the score decreases as the age increases, but the magnitude of the decrease becomes smaller.
C. And directly giving a final decision result to the experts meeting the preset condition.
For example, there are a top journal, a total amount of participating projects exceeds a certain amount, the working experience meets the conditions, and the final decision result is directly given or selected.
Step S102: and training by utilizing three integrated learning models through input dimensional features and initial decision results to form model decision results.
The training is performed by using three integrated learning models and through input dimensional features and initial decision results to form model decision results, and the method comprises the following steps:
A. building three integrated learning models of random forest based on bagging thought, XGboost based on boost thought and GBDT; preferably, the ratio of 8:2 into training and test sets.
The ensemble learning algorithm does not work as a separate machine learning algorithm by itself, but accomplishes the learning task by constructing and combining multiple machine learners. The method is characterized by being a hundred-family-oriented method, and can have higher accuracy in a machine learning algorithm, and the defects that the training process of the model is possibly more complicated and the efficiency is not very high.
B. And (3) utilizing a grid searching mode, taking the mean square error as an evaluation index of the model, carrying out parameter optimization, and preferably selecting the fitting effect of the three ensemble learning models.
The grid search method is an exhaustive search method for specified parameter values, and an optimal learning algorithm is obtained by optimizing parameters of an estimation function through a cross validation method. In short, a model can be given manually, where there are parameters used that are to be modified, and the program runs all the parameters used through an exhaustive method. The maximum tree depth is often used as a parameter to be adjusted in the decision tree.
And then, taking the mean square error as an evaluation index of the model and carrying out parameter optimization. The mean square error, i.e., the standard deviation, is the square root of the variance. The method can better reflect the discrete degree of adjustment of each parameter in the group. In the step, parameters of the three ensemble learning models are optimized through training, so that the fitting effect of the three ensemble learning models is optimal.
C. And storing the three integrated learning models after parameter adjustment, inputting the dimensional characteristics of the scientific research performance to be predicted, and simultaneously keeping the model decision results of the three integrated learning models.
In brief, the dimension characteristics of the three integrated learning models after parameter adjustment are input to be used for testing, and the model decision results of the three integrated learning models can be obtained.
Step S103: determining a rule of data scaling through statistical analysis of past data indexes to finely adjust the initial decision result and form a result candidate set with the model decision result;
wherein the rule for determining data scaling through statistical analysis of past data indicators comprises:
wherein N is an initial decision result; x is all initial decision results; a is the difference value of the upper limit and the lower limit of the initial decision result interval; and B is the lower limit of the interval of the initial decision result.
For example, statistical analysis of expert decision results in recent years can find that, no matter what kind of professional field, the decision results of experts are mostly concentrated in the horizontal interval of [65,90], and in order to distribute the traditional decision results which are generally low closer to the real expert decision results, the traditional decision results of each professional field are placed in [65,90] by using the following data scaling formula, and a result candidate set is formed by the traditional decision results and 3 model decision results.
Wherein 25 is the difference between the upper and lower limits of the interval, and 65 is the lower limit of the interval.
Step S104: and determining the final result for assisting the expert to make a decision according to the average level of the input overall sample data.
In some examples, the final decision result is selected according to the average level of each professional field because the scientific research performance obtained by the ensemble learning is concentrated and does not conform to the rule of the decision result of the expert.
For example, in a total of 13 dimensions:
1) When 9 characteristics of an expert are higher than the average level in the group, judging that the expert is ranked in front of the group, and taking the candidate centralized optimal value as a final result;
2) When 11 characteristics of an expert are lower than the average level in the group, judging that the expert ranks in the group, and taking the worst value in the candidate set as a final result;
3) When the characteristic condition of an expert does not belong to the conditions 1) and 2), judging that the expert is ranked in the middle in the group, and taking the average level of the candidate set as a final result.
In the present application, the strategy for fusing or processing the candidate sets is not to select a common averaging method. Generally, for the regression prediction problem of the numerical class, a commonly used combination strategy is an averaging method, that is, the outputs of several weak learners are averaged to obtain the final prediction output. The simplest average is an arithmetic average, that is the final prediction will take into account the weights of the learning machine. In order to avoid the concentrated scientific research performance obtained by the integrated learning, the ranking of each expert in the group is preliminarily determined by comparing the average level of the overall sample data with the rule of the decision result of the expert, then different selections are made, and the selection is not the best or worst value of the selection, but is based on the selection of the scattered ranking, namely the best selection at the front of the ranking, the mean selection at the middle of the ranking and the worst selection at the back of the ranking. Compared with the existing average method, the processing mode is more reasonable and accords with the rule of an expert decision result.
In summary, the present application provides an expert-aided decision-making method based on ensemble learning, which has the advantages that: on the basis of training by using an integrated learning model, a data scaling method is utilized, so that the result distribution is more in line with the actual situation, the condition that the distribution of the output result of the model is excessively concentrated is improved, and the expert decision is more reliably assisted. The method is easy to implement, is low in cost, avoids the condition that the experts contradict before and after decision making on one hand, and can effectively improve the decision making efficiency of the experts by means of integrated learning on the other hand.
Fig. 2 is a block diagram of an assistant expert decision device based on ensemble learning according to an embodiment of the present application. As shown, the apparatus 200 includes:
a preprocessing module 201, configured to preprocess an input data index to form an initial decision result;
the processing module 202 is configured to train through input dimensional features and initial decision results by using three integrated learning models to form a model decision result; determining a rule of data scaling through statistical analysis of data indexes to finely adjust the initial decision result and form a result candidate set with the model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
It should be noted that, because the contents of information interaction, execution process, and the like between the modules/units of the apparatus are based on the same concept as the method embodiment described in the present application, the technical effect brought by the contents is the same as the method embodiment of the present application, and specific contents may refer to the description in the foregoing method embodiment of the present application, and are not described herein again.
It should be further noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these units can be implemented entirely in software, invoked by a processing element; or can be implemented in the form of hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, each module may be a processing element separately set up, or may be integrated into a chip of the apparatus, or may be stored in a memory of the system in the form of program code, and a processing element of the apparatus calls and executes the functions of each module. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when one of the above modules is implemented in the form of a Processing element scheduler code, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. For another example, these modules may be integrated together and implemented in the form of a System-on-a-Chip (SoC).
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present invention. As shown, the computer device 300 includes: a memory 301, and a processor 302; the memory 301 is used for storing computer instructions; the processor 302 executes computer instructions to implement the method described in fig. 1.
In some embodiments, the number of the memories 301 in the computer device 300 may be one or more, the number of the processors 302 may be one or more, and fig. 3 is taken as an example.
In an embodiment of the present application, the processor 302 in the computer device 300 loads one or more instructions corresponding to processes of an application program into the memory 301 according to the steps shown in fig. 1, and the processor 302 executes the application program stored in the memory 301, thereby implementing the method shown in fig. 1.
The memory 301 may include a Random Access Memory (RAM), and may also include a non-volatile memory (non-volatile memory), for example, at least one disk memory. The memory 301 stores an operating system and operating instructions, executable modules or data structures, or a subset thereof, or an expanded set thereof, wherein the operating instructions may include various operating instructions for implementing various operations. The operating system may include various system programs for implementing various basic services and for handling hardware-based tasks.
The Processor 302 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In some specific applications, the various components of the computer device 300 are coupled together by a bus system that may include a power bus, a control bus, a status signal bus, etc., in addition to a data bus. But for clarity of illustration the various buses have been referred to in figure 3 as a bus system.
In an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the method described in fig. 1.
The present application may be embodied as systems, methods, and/or computer program products, in any combination of technical details. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present application.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer readable program described herein may be downloaded from a computer readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present application may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry can execute computer-readable program instructions to implement aspects of the present application by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
In summary, the method, the device, the equipment and the medium for assisting expert decision based on ensemble learning provided by the application preprocess the input data indexes to form an initial decision result; training by utilizing three integrated learning models through input dimensional features and initial decision results to form model decision results; determining a rule of data scaling through statistical analysis of data indexes so as to finely adjust the initial decision result and form a result candidate set with the model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
The application effectively overcomes various defects in the prior art and has high industrial utilization value.
The above embodiments are merely illustrative of the principles and utilities of the present application and are not intended to limit the invention. Any person skilled in the art can modify or change the above-described embodiments without departing from the spirit and scope of the present application. Accordingly, it is intended that all equivalent modifications or changes which may be made by those skilled in the art without departing from the spirit and technical spirit of the present invention are covered by the claims of the present application.
Claims (10)
1. An expert assistant decision method based on ensemble learning, which is characterized in that the method comprises the following steps:
preprocessing an input data index to form an initial decision result;
training by utilizing three integrated learning models through input dimensional features and initial decision results to form model decision results;
determining a rule of data scaling through statistical analysis of data indexes to finely adjust the initial decision result and form a result candidate set with the model decision result;
and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
2. The method of claim 1, wherein the method of pre-processing the input data metrics comprises any one or more of:
accumulating the data indexes of multiple dimensions to obtain an initial traditional scientific research performance;
converting the age into an age score according to an age conversion formula;
and directly giving a final decision result to the talents meeting the preset conditions.
3. The method of claim 1, wherein the age conversion formula is:
wherein age is age.
4. The method of claim 1 or 2, wherein the data indicators comprise: any one or more of h index score, 1% high cited article score, patent score and total domestic item sum score.
5. The method according to claim 1, wherein the training with three ensemble learning models through the input dimensional features and the initial decision result to form a model decision result comprises:
building three integrated learning models of random forest based on bagging thought, XGboost based on boost thought and GBDT;
by utilizing a grid searching mode, taking the mean square error as an evaluation index of the model and carrying out parameter tuning, so as to preferably select the fitting effect of the three integrated learning models;
and storing the three integrated learning models after parameter adjustment, inputting the dimensional characteristics needing to predict the scientific research performance, and simultaneously keeping the model decision results of the three integrated learning models.
6. The method of claim 1, wherein determining the rule for scaling the data through statistical analysis of past data metrics comprises:
wherein N is an initial decision result; x is all initial decision results; a is the difference value of the upper limit and the lower limit of the initial decision result interval; and B is the lower limit of the interval of the initial decision result.
7. The method of claim 1, wherein the method of determining the result of the final expert assistant decision based on the average level of the input global sample data comprises any one or more of the following:
1) When the characteristics of the expert with the proportion not less than the first preset proportion are all higher than the average level in the group, judging that the expert is ranked ahead in the group, and taking the candidate centralized optimal value as a final result;
2) When the ratio of the features of one expert is not less than the second preset ratio and is lower than the average level in the group, judging that the expert ranks in the group, and taking the worst value in the candidate set as a final result;
3) And when the characteristic condition of an expert does not belong to the above condition, judging that the expert is ranked in the middle in the group, and taking the average level of the candidate set as a final result.
8. An assistant expert decision-making device based on ensemble learning, characterized in that the device comprises:
the preprocessing module is used for preprocessing the input data indexes to form an initial decision result;
the processing module is used for training through input dimensional characteristics and initial decision results by utilizing three integrated learning models to form model decision results; determining a rule of data scaling through statistical analysis of data indexes so as to finely adjust the initial decision result and form a result candidate set with the model decision result; and selecting a final decision result in the candidate set corresponding to each expert according to the average level of the input overall sample data.
9. A computer device, the device comprising: a memory, and a processor; the memory is to store computer instructions; the processor executes computer instructions to implement the method of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer instructions which, when executed, perform the method of any one of claims 1 to 7.
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| CN116384844B (en) * | 2023-06-07 | 2023-09-08 | 广东省科学院广州地理研究所 | Decision method and device based on geographic information cloud platform |
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